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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Monitor Azure OpenAI with Datadog
2023-05-23 · via Datadog | The Monitor blog

Azure OpenAI is a service that helps you develop generative AI applications and custom copilots through OpenAI’s library of resources. With its easy-to-use REST APIs, you can leverage the service to access OpenAI’s powerful models, such as ChatGPT, for your applications while taking advantage of the reliability and security of the Azure platform. Datadog already offers an out-of-the-box integration for OpenAI so you can monitor key performance trends, such as API usage patterns, token consumption, and more. We’re proud to provide the same comprehensive visibility for Azure OpenAI, enabling you to monitor costs and performance issues that are specific to your Azure instances.

As Azure has continued to expand their OpenAI offerings, we’ve enhanced our existing Azure OpenAI integration in turn. With the latest updates to our integration, you can access additional out-of-the-box metrics alongside updated monitors and dashboards for even deeper visibility into model usage and performance. With Datadog’s Azure OpenAI integration, you can identify and troubleshoot performance degradations even faster, as well as track and fine-tune your usage costs on a more granular level.

Complete visibility into Azure OpenAI performance

Powered by Datadog’s existing Azure integration, our Azure OpenAI integration requires no additional setup. Once enabled, Datadog will start collecting metrics from your Azure OpenAI instances. You can use the built-in integration dashboard for an overview of performance across all your instances as well as usage trends for the Azure OpenAI service.

The updated OOTB integration dashboard, including data about token usage and request latency.

On this dashboard, you can view data such as the total number of executed API calls per Azure resource, the latency for these calls, and how many of them were blocked or rate limited. This enables you to quickly spot issues that could impact end-user experience. If calls for your application are regularly being denied due to rate limits, this may indicate that you need to increase the default limits or, alternatively, look into potential optimization strategies for your application.

For even deeper troubleshooting insights, you can also configure Datadog to collect Azure OpenAI logs, which provide you with the necessary context to identify the source of unusual changes in performance. For example, you can review your logs in Datadog Log Management to quickly determine the root cause of a sudden spike in errors for a particular Azure OpenAI instance.

Track usage and costs for your Azure OpenAI instances

Datadog’s Azure integration automatically collects performance data that is unique to Azure OpenAI instances. This ensures that you have the information you need to monitor usage and potential costs for all your AI applications, regardless of how they are deployed. For example, you can get a better picture of the costs for running your customized Azure OpenAI models by tracking the number of processed inference tokens and fine-tuned training hours per model deployment via the integration dashboard.

You can also use our out-of-the-box recommended monitors to automatically receive notifications on problematic usage cost activity. For example, you may want to be alerted when your Azure OpenAI token usage is abnormally high. This helps you quickly investigate and remediate the issue—which could be the result of an unpredictable spike in user traffic or new code added to your application—before you exceed the capacity of your existing resources. You can also use these monitors to detect user activity that may indicate security threats, such as API requests flagged by the Azure OpenAI content filter.

Configuration settings for the Azure OpenAI high token usage monitor.

Start monitoring your Azure OpenAI applications today

Our out-of-the-box Azure integration gives you instant, comprehensive visibility into all of Azure’s supported language models and your Azure OpenAI instances. Check out our documentation to learn more about enabling the integration for your environment. If you don’t already have a Datadog account, you can sign up for a 14-day free trial today.